Online learning is becoming a more and more important aspect of modern life. One major component of the learning process is interaction in the form of questions and answers. As the question-asking audience can come from any domain as far as the area and the level of expertise, it is often challenging for one specialist to handle all kinds of questions from experts and non-experts alike. Many approaches to automatic question answering systems have been proposed to save human efforts by leveraging historical data. Current solutions of question answering systems usually focus on finding an answer by relevance tuning of a computer-implemented model and asking for user clarification if ambiguity exits. In reality, a person asking a technical question may not always know the best way to describe the problem they are experiencing. They may talk a lot about the situation, while not clearly stating the key point. In that case, too much noise may be involved in the question, which makes existing systems of relevance measuring less effective.